Department of Psychology, University of California, Los Angeles, 405 Hilgard Avenue, Los Angeles, CA 90095-1563, USA.
J Exp Psychol Gen. 2010 Nov;139(4):702-27. doi: 10.1037/a0020488.
A fundamental issue for theories of human induction is to specify constraints on potential inferences. For inferences based on shared category membership, an analogy, and/or a relational schema, it appears that the basic goal of induction is to make accurate and goal-relevant inferences that are sensitive to uncertainty. People can use source information at various levels of abstraction (including both specific instances and more general categories), coupled with prior causal knowledge, to build a causal model for a target situation, which in turn constrains inferences about the target. We propose a computational theory in the framework of Bayesian inference and test its predictions (parameter-free for the cases we consider) in a series of experiments in which people were asked to assess the probabilities of various causal predictions and attributions about a target on the basis of source knowledge about generative and preventive causes. The theory proved successful in accounting for systematic patterns of judgments about interrelated types of causal inferences, including evidence that analogical inferences are partially dissociable from overall mapping quality.
对于人类推理理论来说，一个基本的问题是要确定潜在推理的约束条件。对于基于共享类别成员、类比和/或关系模式的推理，似乎归纳的基本目标是做出准确和目标相关的推理，这种推理对不确定性敏感。人们可以在不同抽象层次（包括具体实例和更一般的类别）上使用源信息，再结合先验因果知识，为目标情境构建一个因果模型，这反过来又限制了对目标的推理。我们在贝叶斯推理框架中提出了一个计算理论，并在一系列实验中检验了它的预测（对于我们考虑的情况，是无参数的），实验要求人们根据关于生成和预防原因的源知识，来评估关于目标的各种因果预测和归因的概率。该理论成功地解释了对相关类型因果推理的判断的系统模式，包括证据表明类比推理与整体映射质量部分分离。
